Keyframe extraction can effectively help users quickly understand video content. Generally, keyframes should be representative of the video content and simultaneously be diverse to reduce redundancy. Aiming to find the features of frames and filter out representative frames of the video, we propose a method of keyframe recommendation based on feature intercross and fusion (KFRFIF). The method is inspired by the implied relations between keyframe-extraction problem and recommendation problem. First, we investigate the application of a recommendation framework to the keyframe extraction problem. Second, the architecture of the proposed KFRFIF is put forward. Then, an algorithm for extracting intra-frame image features based on the combination of multiple image descriptors is proposed. An algorithm for extracting inter-frame distance features based on the combination of multiple distance calculation methods is designed. Moreover, A recommendation model based on feature intercross and fusion is put forward. An ablation study is further performed to verify the effectiveness of the submodule. Ultimately, the experimental results on four datasets with five outstanding approaches indicate the superior performance of our approach.

关键帧提取可以有效帮助用户快速理解视频内容。一般来说，关键帧应该能够代表视频内容，同时具有多样性以减少冗余。为了找到帧的特征并过滤出视频的代表性帧，我们提出了一种基于特征交叉与融合的关键帧推荐方法（KFRFIF）。该方法的灵感来自于关键帧提取问题和推荐问题之间的隐含关系。首先，我们研究推荐框架在关键帧提取问题中的应用。其次，提出了所提出的 KFRFIF 的架构。然后，提出了一种基于多个图像描述符组合的帧内图像特征提取算法。设计了一种基于多种距离计算方法相结合的帧间距离特征提取算法。此外，提出了一种基于特征交叉与融合的推荐模型。进一步进行消融研究以验证子模块的有效性。最终，四个数据集和五种出色方法的实验结果表明了我们方法的卓越性能。